When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning

Rongzhi Zhu; Wei Hu; Yi Liu; Yiwei Wang; Zequn Sun

REVIEW 4 major objections 6 minor 1 cited by

When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Prompted to save thinking, a reinforcement-learning-trained reasoning model splits into three distinct modes: skipping thought cuts output length drastically but sacrifices accuracy, while re-engaging in thinking keeps accuracy with…

desk verdict The behavioral taxonomy and the NT accuracy collapse are solid and useful; the mechanistic story is correlational and over-claimed, but the paper deserves a serious referee after revision. read the letter →

arxiv 2505.15276 v1 pith:6IULY3BD submitted 2025-05-21 cs.AI cs.CL

Rongzhi Zhu , Yi Liu , Zequn Sun , Yiwei Wang , Wei Hu This is my paper

classification cs.AIcs.CL

keywords largereasoningmodelsoverthinkingthinkingmodesattentionanalysispromptefficiencyreinforcementlearningQwQ-32Bchain-of-thought

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish why an RL-trained reasoning model like QwQ-32B responds inconsistently when told to save thinking. When the model is given a pre-filled thinking segment ending in '', it sometimes skips further thought, sometimes thinks explicitly again, and sometimes thinks without closing the tag. By examining internal softmax confidence at the token and layer-wise attention patterns, the paper argues that these three modes correspond to distinct internal states that emerge before the first answer token is generated. The practical stakes are efficiency versus reliability: skipping thinking saves over 99% of output tokens but collapses accuracy, while explicit rethinking preserves or even improves accuracy while reducing output length. The paper concludes that current RL training has not produced a stable internal control signal for deciding when thinking is done, so adaptive methods are needed.

What carries the argument

The organizing object is the three-mode taxonomy of NT, ET, and IT. The analysis is carried by three internal measurements: softmax confidence metrics (top-1 probability, entropy, and difference from the second-best candidate) on the prediction of the </think> token after the pre-filled segment; layer-wise attention activation vectors summarized with PCA and a Davies-Bouldin clustering index; and attention from the first generated token to the 'user' role token and to prompt sections. These measurements show that the behavioral split correlates with internal states that are visible before any new token is produced, and that the divergence between modes appears early in the network.

What would settle it

An intervention study would settle the causal question: if early-layer attention is the cause, steering representations away from the pre-filled thinking section toward the user token before the first generated token should flip NT samples into ET or IT behavior. If mode rates do not move under such activation steering, the early attention patterns are not the causal mechanism, even though the behavioral taxonomy and performance differences could remain true.

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Extended reading notes

Core claim

The central discovery is that when QwQ-32B receives a pre-filled thinking prompt, its behavior separates into three modes: no thinking (NT), explicit thinking (ET), and implicit thinking (IT). At the moment the model predicts the </think> token that would end the pre-filled segment, NT questions show higher top-1 probability, lower entropy, and a larger gap to the second-best token, indicating the model is 'ready' to answer directly. Layer-wise attention activations between NT and ET diverge sharply from around layer 5 onward, and NT samples concentrate attention on the pre-filled thinking section rather than on the user's question. Behaviorally, NT shrinks output length by over 99% but drops accuracy from 94.09% to 37.76% on GSM8K and from 99.15% to 52.54% on MATH500; ET, by contrast, cuts output length by roughly 13-33% while preserving or slightly exceeding baseline accuracy. The paper's claim is that the mode is not a formatting artifact but an early, sustained internal decision about whether the prompt's 'thinking is finished' signal should be trusted.

Load-bearing premise

The load-bearing premise is that the early-layer attention divergence and </think>-token confidence are causes of the mode decision rather than effects or correlates of it; the paper observes correlations and clustering but performs no intervention to establish that causal direction.

Editorial extensions

If this is right

A single save-thinking prompt cannot be trusted as a general efficiency lever: for questions where the model chooses NT, the length savings are enormous but accuracy collapses, so naive deployment would be unsafe.
The confidence in the </think> token correlates with NT accuracy, meaning the pre-filled segment itself could serve as a cheap indicator of when direct answering is likely to succeed.
ET and IT show that the model can compress its visible reasoning without losing accuracy, suggesting that efficiency gains are available if the model can be steered into these modes rather than NT.
Because the attention divergence appears in early layers, early-layer representations could potentially be used as a control signal for routing questions to different reasoning policies.
The behavioral divergence of this RL-trained model, contrasted with SFT-distilled models that follow the same prompt more consistently, points to training objective as a key factor in thinking control.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the early-layer divergence is causal, a lightweight probe on early activations could decide at runtime whether to skip thinking, re-engage thinking, or keep standard reasoning, adapting per question rather than per prompt.
The confidence-accuracy correlation in NT suggests a threshold-based policy: only allow skipping when </think> confidence is high, otherwise force a short verification pass.
The near-miss errors in NT, where answers differ by a single digit, suggest that skipping removes a verification step rather than the underlying computation; a minimal second-pass check might recover much of the lost accuracy at a fraction of full-thinking cost.
Extending this analysis to other RL-trained reasoning models and to non-mathematical reasoning tasks would test whether the three modes are a general property of RL-trained chain-of-thought models or specific to the QwQ-32B checkpoint and math distribution.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things worth knowing. The behavioral taxonomy is real, and the performance table is the backbone: when QwQ-32B actually bypasses thinking, GSM8K accuracy collapses from 94.09% to 37.76%, while ET keeps accuracy and still saves a third of the tokens. That is a credible, useful caution for anyone using save-thinking prompts on RL-trained reasoners. The second thing is that the mechanistic story is not established. The confidence and attention analyses are correlational, the confidence measurement is close to circular, and the causal wording in Sections 3.2–3.4 goes beyond what the experiments show.

What is genuinely new: the three-mode categorization for QwQ-32B, the -token confidence measurements, the layer-wise attention separation, and the confidence–accuracy curve for NT. The behavioral divergence itself was already noted by Liu et al. and Ma et al., but the systematic annotation across GSM8K and MATH500, plus the internal-state measurements, are legitimate additions. Table 4 is the real contribution: it quantifies the accuracy–efficiency tradeoff cleanly, and Figure 6 gives practitioners a potential trigger for adaptive inference.

The soft spots are proportionate but real. The confidence numbers in Table 2 are computed at the prefilled token after the mode is already known from the subsequent output, so the finding that NT has higher termination confidence is partly built into the mode definition. The attention analyses are on outputs that already belong to a mode; no intervention or counterfactual separates cause from correlate. The paper's language—"precipitates", "shaping reasoning strategies", "leads the LRM to bypass"—overstates the evidence. There are also no error bars, no significance tests, and no inter-annotator agreement for the manual mode labels. The IT cell on MATH500 has n=3, so the three-mode story on that dataset is really two modes. The attention-to-user-token result could be a positional artifact: the user token is the first input token, and lower attention to it in NT may simply reflect more attention to the prefilled thinking block. The paper's Limitations section mentions dataset scope and missing mitigation strategies, but it never acknowledges the correlational design or the annotation reliability issue, which is a notable omission.

That said, the central behavioral claim holds up. NT is real, costly, and worth studying; ET is the interesting efficient regime. The taxonomy and the performance numbers will be cited by people building adaptive reasoning pipelines. The paper should go to peer review, but with a request for major revision: release the annotations, add statistical tests and inter-annotator agreement, and either soften the mechanistic language or run a real intervention. The interpretability sections should be framed as exploratory, not explanatory.

Referee Report

4 major / 6 minor

Summary. This paper studies QwQ-32B, an RL-trained reasoning model, under a prompt that pre-fills a 'finished thinking' segment and asks the model to answer directly. The authors manually label the resulting outputs into three modes—no thinking (NT), explicit thinking (ET), and implicit thinking (IT)—on GSM8K and MATH500. They report three internal-state analyses: next-token confidence at the pre-filled </think> boundary (Table 2), layer-wise attention activation clustering (Figures 2–3), and attention to prompt sections (Tables 3, Figures 4–5). They then compare accuracy and output length against a no-prefill baseline (Table 4), finding that NT saves length but hurts accuracy, while ET and IT preserve accuracy with shorter outputs. The paper concludes that RL-trained LRMs show inconsistent internal preparation for saving thinking, and it promises to release the manual mode annotations.

Significance. If the behavioral taxonomy and performance comparison are accepted, the paper makes a useful empirical contribution: it quantifies a real failure mode of RL-trained LRMs under efficiency prompting and identifies a regime (ET) where length is reduced without an obvious accuracy loss. The effect sizes for NT are striking and credible, e.g., GSM8K accuracy drops from 94.09% to 37.76% while median length collapses to 35 tokens. The promise of releasing manual mode annotations is valuable for future work. However, the mechanistic claims in Sections 3.2–3.4 are currently correlational and partly circular; the causal framing is not established. The paper's significance as a 'mechanistic analysis' is therefore conditional on reanalysis or substantially softened conclusions.

major comments (4)

[§3.2–§3.4] The causal framing is not supported by the evidence. Section 3.3's Findings state that early-layer attention dynamics 'shape reasoning strategies,' and Section 3.4 states that an internal state 'leads the LRM to bypass further reasoning,' but all measurements are correlational: the confidence value in Table 2 is taken at the pre-filled boundary before the mode-defining tokens are generated, and the attention vectors in Figures 2–5 are computed on inputs that are labeled by the subsequent output. No intervention, counterfactual, or control for the first generated token's identity is reported. These observations can distinguish or predict modes without explaining their cause, so the mechanistic contribution as written is not established. I ask for an intervention study (e.g., steering attention or logits at an early layer and observing mode shifts) or a thorough reframing to descriptive language.
[§3.2, Table 2] The 'confidence in thinking termination' measurement needs a precise specification of the tensor position. The prompt already contains a pre-filled '</think>' token, so it is not clear whether the softmax is computed at that pre-filled position under teacher forcing or at the first free generation position. If the former, the top-1 being '</think>' is by construction and the correlation with the later mode is a predictive correlate, not a decision made by the model; if the latter, the claim that the top-1 is consistently '</think>' needs verification. Please state the exact position and avoid wording that the LRM 'predicts' a token that is already present in the input.
[§4.1, Table 4] The central performance claim that ET 'preserves accuracy' is made without uncertainty quantification. The ET-vs-baseline accuracy differences are +0.74 percentage points on GSM8K and +1.85 on MATH500, with roughly 300–380 questions per group; no confidence intervals or significance tests are reported. The NT length/accuracy collapse is robust, but the 'no trade-off' conclusion for ET (and any claim of superiority over baseline) requires per-question paired bootstrap intervals or an equivalent test. Please add them and, if the differences are not significant, soften the claim.
[§3.1, Table 1] The mode labels are the foundation of every subsequent analysis but are produced by manual inspection with no annotation protocol, no inter-annotator agreement, and no treatment of ambiguous cases. This is especially important for the IT category, which is defined by the absence of a '</think>' tag and has n=75 and n=3 in the two datasets. Please release a detailed rubric, report agreement on at least a subset with multiple annotators, and quantify the sensitivity of Tables 2–4 to labeling disagreements.

minor comments (6)

[§3.2, last paragraph] The phrase 'and and favoring' contains a duplicated word; also, the sentence 'This confidence gap offers a key insight: higher confidence in thinking termination may be necessary for skipping reasoning' should be marked as a hypothesis rather than a finding.
[Tables 2 and 3] The rows labeled 'Average' are weighted by the per-dataset sample sizes, but the captions do not say so; please state this explicitly.
[Tables 3–4 and Figure 5] MATH500 has only three IT samples, yet IT values for MATH500 are reported without confidence intervals; either remove them or clearly mark them as anecdotal.
[§3.3, Eq. (1)] The Davies-Bouldin Index formula is only sketched; please define S1, S2, and D1,2 explicitly and state how the layer-wise attention activation vectors are formed before clustering.
[Figure 4] The green dashed line is said to represent the average value of MATH500 IT samples, but no numerical value is given in the figure; please add the value or clearly reference it from Table 3.
[Appendix A] The final-answer extraction uses regular expressions followed by manual verification, but no details on the regex or the manual verification protocol are provided; a few examples would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No definitional circularity found; the paper's confidence and attention analyses are correlational, but they are not numerically identical to the mode definitions by construction.

full rationale

The paper's central claims are an empirical behavioral taxonomy and post-hoc internal-state measurements, not a derivation from fitted parameters. NT, ET, and IT are defined by observable output structure: NT 'bypasses further thinking and directly generates the answer,' ET 'appends an additional </think> tag,' and IT 'does not output the </think> tag.' The Section 3.2 confidence is computed at the pre-filled </think> position, before the mode-defining continuation is generated, and the Section 3.3-3.4 attention analyses are computed at the first generated token, which is part of the mode-defining behavior. These are correlated with the mode labels, and the paper's causal language ('shaping reasoning strategies,' 'precipitates') is stronger than the correlational evidence supports; that is a validity limitation rather than a circular reduction. No equation in the paper makes a measured quantity numerically equal to a mode label, and no fitted parameter is renamed as a prediction. The only self-citations (Zhu et al. 2025; Luo et al. 2024) appear as background in the introduction and are not load-bearing. The behavioral comparisons against the standard-prompt baseline are evaluated on the same question groups but do not use the mode-defining measurements as inputs. Thus no circular step meets the quoted-reduction standard.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no fitted numerical parameters. Its central claims rest on interpretive assumptions about what attention and confidence mean, on the representativeness of QwQ-32B, on the reliability of manual labeling, and on the validity of within-group baseline comparisons.

assumptions (5)

domain assumption QwQ-32B is representative of RL-trained native LRMs.
The study uses only QwQ-32B and generalizes to RL-trained LRMs; other RL models may behave differently. See Section 3.1.
domain assumption Attention scores and softmax confidence are valid indicators of internal reasoning state and are causally upstream of behavior.
The paper interprets correlational attention and confidence differences as mechanisms; no causal intervention is performed. See Sections 3.2 to 3.4.
domain assumption Exact match plus manual verification is a correct accuracy measure.
They combine automatic EM with manual checks, but manual checks are not inter-annotator validated. See Appendix A.
domain assumption The three-mode taxonomy is exhaustive and reliably annotated.
Manual annotation without agreement metrics; IT mode is rare and potentially noisy. See Section 3.1.
domain assumption Performance comparison within behavior-defined groups is a valid counterfactual.
The baseline for a group uses the same questions without the prefilled prompt, but questions are not randomized to modes; modes are model-selected. See Section 4.1.

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Pith. "Pith review of When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning." pith.science (2026). https://pith.science/paper/6IULY3BD

@misc{pith2026250515276,
  author       = {Pith},
  title        = {Pith review of: When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6IULY3BD}},
  note         = {Machine review of arXiv:2505.15276}
}

read the original abstract

Large reasoning models (LRMs) have significantly advanced performance on complex tasks, yet their tendency to overthink introduces inefficiencies. This study investigates the internal mechanisms of reinforcement learning (RL)-trained LRMs when prompted to save thinking, revealing three distinct thinking modes: no thinking (NT), explicit thinking (ET), and implicit thinking (IT). Through comprehensive analysis of confidence in thinking termination, attention from thinking to generation, and attentional focus on input sections, we uncover key factors influencing the reasoning behaviors. We further find that NT reduces output length at the cost of accuracy, while ET and IT maintain accuracy with reduced response length. Our findings expose fundamental inconsistencies in RL-optimized LRMs, necessitating adaptive improvements for reliable efficiency.

Figures

Figures reproduced from arXiv: 2505.15276 by the authors.

**Figure 1.** Examples of the three modes of QwQ-32B under the save-thinking instructions. The final answers are underlined. The thought is marked in italics. The token </think> marks the end of the thought. Our manual annotations of the three thinking modes in the GSM8K and MATH500 datasets will be publicly available to support future research. 2 Related Work 2.1 Large Reasoning Models Large language models (LLMs) have demonstra… view at source ↗

**Figure 2.** PCA visualization of attention activation from the last layer for all samples in GSM8K and MATH500. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Davies-Bouldin Index (less is better) calcu [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: The density distribution of attention scores under different thinking modes. Implicit thinking samples in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Attention scores across prompt sections for [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Top1 softmax probability vs. acc. for NT. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: Attention scores across prompt sections for [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

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Forward citations

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Rongzhi Zhu, Xiangyu Liu, Zequn Sun, Yiwei Wang, and Wei Hu. 2025. https://doi.org/10.48550/arXiv.2502.14245 Mitigating lost-in-retrieval problems in retrieval augmented multi-hop question answering . CoRR, abs/2502.14245

2025 doi arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Pranjal Aggarwal and Sean Welleck. 2025. https://doi.org/10.48550/arXiv.2503.04697 L1: controlling how long A reasoning model thinks with reinforcement learning . CoRR, abs/2503.04697

arXiv doi:10.48550/arxiv.2503.04697 2025

[4] [4]

Avinash Anand, Mohit Gupta, Kritarth Prasad, Navya Singla, Sanjana Sanjeev, Jatin Kumar, Adarsh Raj Shivam, and Rajiv Ratn Shah. 2024. https://doi.org/10.48550/arXiv.2404.13099 Mathify: Evaluating large language models on mathematical problem solving tasks . CoRR, abs/2404.13099

work page Pith review arXiv doi:10.48550/arxiv.2404.13099 2024

[5] [5]

Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, and Wanxiang Che. 2025. https://doi.org/10.48550/arXiv.2503.09567 Towards reasoning era: A survey of long chain-of-thought for reasoning large language models . CoRR, abs/2503.09567

arXiv doi:10.48550/arxiv.2503.09567 2025

[6] [6]

Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu. 2024. https://doi.org/10.48550/arXiv.2412.21187 Do NOT think that much for 2+3=? on the overthinking of o1-like llms . CoRR, abs/2412.21187

arXiv doi:10.48550/arxiv.2412.21187 2024

[7] [7]

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021. https://arxiv.org/abs/2110.14168 Training verifiers to solve math word problems . CoRR, abs/2110.14168

arXiv 2021

[8] [8]

DeepSeek - AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai...

arXiv doi:10.48550/arxiv.2501.12948 2025